In sporting activities there are times when sportsmen perform jumps, which are characterized by continued motion through the air without mechanical assistance. Such jumps are often performed in such sports as water and alpine skiing, snowboarding, wakeboarding, motorcycling, biking, gymnastics, high jump, and others. In such sports the duration of the jump and the height of the jump are of most interest to the sportsmen and spectators.
In the past, duration of the jump has been determined by observing a sportsman in real time or carefully inspecting a video recording to determining the start and end points of a jump visually. An observer can start a stopwatch when he things that sportsman left the ground (or water) and stop the stopwatch when person appears to have landed. This method works relatively well, especially when a slow motion video recording is used and then examined frame by frame. However, such determination requires a second person making video and then a labor consuming process of manual examining video and timing motion frame by frame.
With advances in electronic sensors and computer processing power, various research has been done to detect and measure sports jumps using sensors attached to the sportsman or his equipment.
In U.S. Pat. No. 5,724,265 to Huthings, the inventor proposed to install contact sensors in a runner's shoes. When both sensors do not measure any contact, a person is determined to be in the air. In principle, such approach may be used for other sports but it is not very practical for many sports, such as skiing and wakeboarding.
Volk, et al., in U.S. Pat. No. 6,539,336 proposes to measure energy expanded by the skier and skier vibration. However, it is not clear how power can be reliably measured and how it is related to airtime.
With advances in accelerometer sensors, a much more efficient method of air time determination became possible. During the jump, the only forces imposed upon sportsmen are generally Earth gravity and air resistance. For the terrestrial and water sports, such as snowboarding, bike jumps, wake boarding, etc., the air resistance is very small, therefore, this motion can be considered as a “free fall”, as is disclosed in Dictionary of Physics, Vi edited by Valerie H. Pitt, Penguin, 1977. This is true even when the body actually moves upward at the initial stage of the jump. Alternatively, during skydiving, the jumper quickly reaches so called ‘terminal velocity” where air resistance becomes equivalent to the force of gravity and cannot be disregarded. See the article “Free Fall,” http://en.wikipedia.org/wiki/Free_fall.
It is well known according to the laws of physics that, during free fall, an accelerometer sensor that is attach to that body should show zero signal in any direction. See the article “Free Fall,” http://en.wikipedia.org/wiki/Free_fall. This is also known from various physics textbooks. Therefore, to detect a free fall is essentially the same as to determine that all accelerometer signals are zero. However, this fact, while well known theoretically, is not so easy to realize in a real world practical situation.
Vock, et al., in U.S. Pat. No. 7,640,135, teach that free fall should be detected when “ . . . summing acceleration signals from the tri-axial accelerometers, wherein the acceleration signals sum to zero when the sportsman is in free-fall.” Unfortunately, real world signals rarely sum up to zero due to sensor noise and sensor calibration errors. The same inventors disclose improved criteria in U.S. Pat. No. 7,860,666, wherein it is proposed that, instead of looking for a virtual zero signal, the signal should be compared with a predefined threshold on the acceleration signal value. In particular, the patent discloses “ . . . determining a period of free-fall comprising timing a duration of acceleration signals below an acceleration floor, wherein acceleration signals above the floor indicate an end to the free-fall period.”
Again, real world situations often do not allow the selection of one accelerometer value which sufficiently separates jumps from non jumps. FIG. 1 presents accelerometer data collected by a skier during a downhill run with jumps. For data collection a smart phone with built in accelerometers and GPS was used. The phone was kept in a breast pocket of the skier's jacket during the run. The jumps were performed near sample number 500 and near sample 800. FIG. 1 presents a sum of tri-axis accelerometer during this run. It is clear that while there are two jumps performed during this sample period, there are no samples in this segment where sum of 3-axis acceleration is equal to zero or even very close to zero. It is also clear that even within a jump the acceleration value could vary significantly and a single threshold would not allow a reliable detection. This is due to several factors.
Firstly, accelerometers have noise and bias, and if not well calibrated, can generate erroneously large readings. Secondly, the sum of acceleration signals could be small when individual directions have large but opposite sign signals. Another source of large acceleration during jump could be skier rotation. During rotation, accelerometers measure centrifugal acceleration that can be significant. Another possible reason is that very often a measuring device is not rigidly connected to the skier or his equipment but is a free standing device, such as when a smart phone with GPS and accelerometer sensors is used. Such device can be loosely positioned in a pocket and move independently on the user, registering additional shocks when bumping around inside the pocket.
In addition to the use of amplitude of individual accelerometers to detect free fall, U.S. Pat. No. 7,640,135 teaches that free fall can be detected when acceleration signals are changing at a low frequency relative to the high frequency acceleration signal that is generated during motion over ground or water. Similarly, Vock et al., in U.S. Pat. No. 7,860,666, use the absence of high oscillation to detect and measure air time by “ . . . processing the accelerometer data comprising determining an absence of vibration to identify loft of the sportsman”. Vock et al. suggest using a microphone to record vibration and determining “loft” by the absence of such vibration in the microphone signal.
Unfortunately, many real world situations are more complicated. FIG. 2 shows accelerometer data used in FIG. 1. As can be seen in FIG. 2, the acceleration signal during jumps has a frequency comparable to such signal during non jump motion. In some real world situations “low frequency acceleration” criteria often does not work and leads to non-detection or false-detection of significant jumps.
One of the characteristic parameters of a jump should be a presence of a landing shock when all the energy of the flight must be quickly absorbed. While landing shock is a very attractive concept, in practice it needs to be detected and measured in order to be useful.
Another problem is that other activities, e.g. walking, produce accelerometer signals that by many characteristics appear very similar to the signals recorded during ski or other sport jumps.
FIG. 3 presents acceleration data collected during a walk, with accelerometer sensors kept in a pants pocket. The figure shows that a regular walk can generate very high acceleration values that appear like a landing shock, and can also generate very low acceleration values that may appear as free falls.
Vock, et al., also propose various methods for filtering out erroneous jump detections. In particular, they propose to filter jumps by velocity value or the length of the suspected jump.
These limitations are useful, but still not sufficient. Any hard threshold will be too small for some jumps and too large for some false alarms. As an example, velocity of a high speed ski lift is comparable with low speed skiing, and there is often a detectable shock when a skier gets on and off the lift chair. A hard jump duration limit is not very reliable either. For example, a snowboarder might have a series of small jumps when getting on and off rails and benches, and such jumps would not be reliably detected.
We conclude from the above examples that there is no one parameter that can reliably identify a jump, but rather there are a large number of very different parameters which must be considered in their interactions to make a reliable jump identification and air time measurement. However, trying to fit all of these parameters using thresholds and binary logic creates a decision algorithm that quickly becomes extremely complicated, and at some point it becomes virtually impossible to create an effective and manageable algorithm using binary logic and thresholds.